GeoThermalCloud: Geothermal Machine Learning Analysis: Utah¶
This notebook is a part of the GeoThermalCloud.jl: Machine Learning Framework for Geothermal Exploration.
This notebook is a part of the GeoThermalCloud.jl: Machine Learning Framework for Geothermal Exploration.
If GeoThermalCloud is not installed, first execute in the Julia REPL:
import Pkg
Pkg.add("GeoThermalCloud")
Pkg.add("NMFk")
Pkg.add("Mads")
Pkg.add("DelimitedFiles")
Pkg.add("JLD")
Pkg.add("Gadfly")
Pkg.add("Cairo")
Pkg.add("Fontconfig")
Pkg.add("Kriging")
Pkg.add("GMT")
Pkg.add("Images")
import GeoThermalCloud
import NMFk
import Mads
import DelimitedFiles
import JLD
import Gadfly
import Cairo
import Fontconfig
import Kriging
import GMT
import Images
┌ Info: TensorLy is available └ @ NTFk /Users/vvv/.julia/dev/NTFk/src/NTFkTensorly.jl:8
Xdat, headers = DelimitedFiles.readdlm("utah/data/utah_geothermal_data.csv", ',', header=true);
Set coordinates:
xcoord = Array{Float32}(Xdat[:, 2])
ycoord = Array{Float32}(Xdat[:, 1]);
Plot data locations on a map:
GMT.grdimage("utah/maps/utah.nc", proj=:Mercator, shade=(azimuth=100, norm="e0.8"),
color=GMT.makecpt(color=:grayC, transparency=10, range=(0,5000,500), continuous=true),
figsize=8, conf=(MAP_FRAME_TYPE="plain", MAP_GRID_PEN_PRIMARY="thinnest,gray,.",
MAP_GRID_CROSS_SIZE_SECONDARY=0.1, MAP_FRAME_PEN=0.5, MAP_TICK_PEN_PRIMARY=0.1,
MAP_TICK_LENGTH_PRIMARY=0.01), frame=(axis="lrtb"))
GMT.plot!(xcoord, ycoord, fill=:cyan, marker=:c, markersize=0.1, coast=(proj=:Mercator,
DCW=(country="US.UT", pen=(0.5,:black))),
fmt=:png, savefig="utah/maps/locations");
Images.load("utah/maps/locations.png")
We can use attribute names from the header in the input file.
However, the names are short.
For better understanding the variable names in the plots generated bellow, we are defining short and long attribute names:
attributes = ["Temperature", "Quartz", "Chalcedony", "pH", "TDS", "Al", "B", "Ba", "Be", "Br", "Ca", "Cl", "HCO3", "K", "Li", "Mg", "Na", "δO18"]
attributes_long = ["Temperature (C)", "GTM quartz (C)", "GTM chalcedony (C)", "pH ()", "TDS (ppm)", "Al (ppm)", "B (ppm)", "Ba (ppm)", "Be (ppm)", "Br (ppm)", "Ca (ppm)", "Cl (ppm)", "HCO3 (ppm)", "K (ppm)", "Li (ppm)", "Mg (ppm)", "Na (ppm)", "δO18 (‰)"];
Set empty data entries to NaN:
Xdat[Xdat .== ""] .= NaN;
Convert to Float32:
X = convert.(Float32, Xdat[:,3:end]);
Rescale δO18 data (‰):
X[:,18] .+= 20;
Set variables for the number of attributes and points:
nattributes = length(attributes_long)
npoints = size(Xdat, 1)
5218
Plot histograms and compute data statistics:
NMFk.datanalytics(X, attributes_long; dims=2);
┌ Info: Temperature (C) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Temperature (C): Min 0.1 Max 261.0 StdDev 9.497179 Skewness 10.095466 Count 5214 ┌ Info: GTM quartz (C) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
GTM quartz (C): Min -50.870045 Max 222.28357 StdDev 26.329222 Skewness 0.24256101 Count 3460 ┌ Info: GTM chalcedony (C) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
GTM chalcedony (C): Min -81.64773 Max 208.67426 StdDev 27.450817 Skewness 0.3369881 Count 3460 ┌ Info: pH () └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
pH (): Min 3.4 Max 10.2 StdDev 0.44822767 Skewness -0.45681924 Count 3941 ┌ Info: TDS (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
TDS (ppm): Min 122.0 Max 329000.0 StdDev 78777.95 Skewness 2.9479313 Count 312 ┌ Info: Al (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Al (ppm): Min 0.0 Max 1.251 StdDev 0.10527698 Skewness 9.031239 Count 221 ┌ Info: B (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
B (ppm): Min 0.0 Max 76.5 StdDev 2.6950865 Skewness 15.190172 Count 2284 ┌ Info: Ba (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Ba (ppm): Min 0.002 Max 4.5 StdDev 0.2898333 Skewness 11.825817 Count 310 ┌ Info: Be (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Be (ppm): Min 5.3333333e-5 Max 0.013 StdDev 0.00089295243 Skewness 10.968073 Count 244 ┌ Info: Br (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Br (ppm): Min 0.0 Max 84.0 StdDev 11.680384 Skewness 3.435088 Count 621 ┌ Info: Ca (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Ca (ppm): Min 0.6 Max 2566.6667 StdDev 262.82578 Skewness 4.424904 Count 3772 ┌ Info: Cl (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Cl (ppm): Min 0.6 Max 240000.0 StdDev 28681.555 Skewness 5.21565 Count 3923 ┌ Info: HCO3 (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
HCO3 (ppm): Min 37.0 Max 9217.0 StdDev 329.8163 Skewness 22.165213 Count 915 K (ppm): Min 0.0 Max 13000.0 StdDev 1063.7937 Skewness 6.380783 Count 3177 Li (ppm): Min 0.0 Max 970.0 StdDev 90.79041 Skewness 6.6990304 Count 551 ┌ Info: K (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54 ┌ Info: Li (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54 ┌ Info: Mg (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
Mg (ppm): Min 0.0 Max 8500.0 StdDev 663.7361 Skewness 6.581147 Count 3760 Na (ppm): Min 0.5 Max 160000.0 StdDev 17983.25 Skewness 4.946034 Count 3437 δO18 (‰): Min 2.1499996 Max 16.36 StdDev 2.4535506 Skewness 2.5710073 Count 368 Name Min Max StdDev Count (non-NaN's) Temperature (C) 0.1 261.0 9.497179 10.095466 5214 GTM quartz (C) -50.870045 222.28357 26.329222 0.24256101 3460 GTM chalcedony (C) -81.64773 208.67426 27.450817 0.3369881 3460 pH () 3.4 10.2 0.44822767 -0.45681924 3941 TDS (ppm) 122.0 329000.0 78777.95 2.9479313 312 Al (ppm) 0.0 1.251 0.10527698 9.031239 221 B (ppm) 0.0 76.5 2.6950865 15.190172 2284 Ba (ppm) 0.002 4.5 0.2898333 11.825817 310 Be (ppm) 5.3333333e-5 0.013 0.00089295243 10.968073 244 Br (ppm) 0.0 84.0 11.680384 3.435088 621 Ca (ppm) 0.6 2566.6667 262.82578 4.424904 3772 Cl (ppm) 0.6 240000.0 28681.555 5.21565 3923 HCO3 (ppm) 37.0 9217.0 329.8163 22.165213 915 K (ppm) 0.0 13000.0 1063.7937 6.380783 3177 Li (ppm) 0.0 970.0 90.79041 6.6990304 551 Mg (ppm) 0.0 8500.0 663.7361 6.581147 3760 Na (ppm) 0.5 160000.0 17983.25 4.946034 3437 δO18 (‰) 2.1499996 16.36 2.4535506 2.5710073 368 ┌ Info: Na (ppm) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54 ┌ Info: δO18 (‰) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54 ┌ Info: Attributes └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:70
Note that the data entries for TDS, Al, and δO18 are heavily missing.
Even though the dataset is very sparse, our ML methods can analyze the inputs.
Most of the commonly used ML methods cannot process datasets that are sparse.
Furthermore, different attributes in the Great Basin dataset cover different areas.
This is demonstrated in the maps generated below.
coord = permutedims([xcoord ycoord])
xgrid, ygrid = NMFk.griddata(xcoord, ycoord; stepvalue=0.1)
for i = 1:nattributes
inversedistancefield = Array{Float64}(undef, length(xgrid), length(ygrid))
v = X[:,i]
iz = .!isnan.(v)
icoord = coord[:,iz]
v = v[iz]
for (i, x) in enumerate(xgrid), (j, y) in enumerate(ygrid)
inversedistancefield[i, j] = Kriging.inversedistance(permutedims([x y]), icoord, v, 2; cutoff=1000)[1]
end
imax = NMFk.maximumnan(inversedistancefield)
imin = NMFk.minimumnan(inversedistancefield)
NMFk.plotmatrix(rotl90(inversedistancefield); quiet=false, filename="utah/maps/Attribute_$(attributes[i])_map_inversedistance.png", title="$(attributes[i])", maxvalue=imin + (imax - imin)/ 2)
end
Attribute values are log-transformed to better capture the order of magnitude variability.
All attributes except for Quartz, Chalcedony and pH are log-transformed (Quartz and Chalcedony have negative values).
logv = [true, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true]
[attributes logv]
18×2 Matrix{Any}:
"Temperature" true
"Quartz" false
"Chalcedony" false
"pH" false
"TDS" true
"Al" true
"B" true
"Ba" true
"Be" true
"Br" true
"Ca" true
"Cl" true
"HCO3" true
"K" true
"Li" true
"Mg" true
"Na" true
"δO18" true
NMFk.datanalytics(X, attributes; dims=2, logv=logv);
┌ Info: Temperature: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
Temperature: Min -1.0 Max 2.4166405 StdDev 0.19028741 Skewness -0.5570712 Count 5214 Quartz: Min -50.870045 Max 222.28357 StdDev 26.329222 Skewness 0.24256101 Count 3460 Chalcedony: Min -81.64773 Max 208.67426 StdDev 27.450817 Skewness 0.3369881 Count 3460 ┌ Info: Quartz └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54 ┌ Info: Chalcedony └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54 ┌ Info: pH └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:54
pH: Min 3.4 Max 10.2 StdDev 0.44822767 Skewness -0.45681924 Count 3941 ┌ Info: TDS: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
TDS: Min 2.0863597 Max 5.5171957 StdDev 0.8672777 Skewness 1.4807001 Count 312 Al: Min -4.3979397 Max 0.09725732 StdDev 0.8454361 Skewness -0.37494856 Count 221 ┌ Info: Al: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51 ┌ Info: B: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
B: Min -3.30103 Max 1.8836614 StdDev 0.77384454 Skewness 0.08153698 Count 2284 ┌ Info: Ba: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
Ba: Min -2.69897 Max 0.6532125 StdDev 0.40884706 Skewness -0.013896148 Count 310 Be: Min -4.273001 Max -1.8860567 StdDev 0.46150032 Skewness -0.83869493 Count 244 ┌ Info: Be: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51 ┌ Info: Br: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
Br: Min -3.0 Max 1.9242793 StdDev 1.0616237 Skewness 0.5956521 Count 621 ┌ Info: Ca: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
Ca: Min -0.22184873 Max 3.4093695 StdDev 0.43120933 Skewness 0.5738154 Count 3772 Cl: Min -0.22184873 Max 5.3802114 StdDev 1.0519629 Skewness 1.1927316 Count 3923 HCO3: Min 1.5682018 Max 3.9645896 StdDev 0.22752158 Skewness 0.051133487 Count 915 ┌ Info: Cl: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51 ┌ Info: HCO3: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51 ┌ Info: K: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
K: Min -2.0 Max 4.1139436 StdDev 0.8480291 Skewness 1.8444492 Count 3177 ┌ Info: Li: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
Li: Min -3.9586072 Max 2.9867718 StdDev 1.4681796 Skewness 0.6889145 Count 551 Mg: Min -2.0 Max 3.929419 StdDev 0.5826601 Skewness 1.142913 Count 3760 ┌ Info: Mg: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51 ┌ Info: Na: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51
Na: Min -0.30103 Max 5.20412 StdDev 0.985548 Skewness 1.3556563 Count 3437 δO18: Min 0.33243838 Max 1.2137833 StdDev 0.16311567 Skewness 1.1355046 Count 368 Name Min Max StdDev Count (non-NaN's) Temperature -1.0 2.4166405 0.19028741 -0.5570712 5214 Quartz -50.870045 222.28357 26.329222 0.24256101 3460 Chalcedony -81.64773 208.67426 27.450817 0.3369881 3460 pH 3.4 10.2 0.44822767 -0.45681924 3941 TDS 2.0863597 5.5171957 0.8672777 1.4807001 312 Al -4.3979397 0.09725732 0.8454361 -0.37494856 221 B -3.30103 1.8836614 0.77384454 0.08153698 2284 Ba -2.69897 0.6532125 0.40884706 -0.013896148 310 Be -4.273001 -1.8860567 0.46150032 -0.83869493 244 Br -3.0 1.9242793 1.0616237 0.5956521 621 Ca -0.22184873 3.4093695 0.43120933 0.5738154 3772 Cl -0.22184873 5.3802114 1.0519629 1.1927316 3923 HCO3 1.5682018 3.9645896 0.22752158 0.051133487 915 K -2.0 4.1139436 0.8480291 1.8444492 3177 Li -3.9586072 2.9867718 1.4681796 0.6889145 551 Mg -2.0 3.929419 0.5826601 1.142913 3760 Na -0.30103 5.20412 0.985548 1.3556563 3437 δO18 0.33243838 1.2137833 0.16311567 1.1355046 368 ┌ Info: δO18: log10-transformed └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:51 ┌ Info: Attributes └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPreprocess.jl:70
Xnl, xlmin, xlmax, zflag = NMFk.normalizematrix_col(X; logv=logv);
nkrange = 2:10
2:10
resultdir = "utah/results";
nruns = 100;
# W, H, fitquality, robustness, aic = NMFk.execute(Xnl, nkrange, nruns; cutoff=0.3, resultdir=resultdir, casefilename="nmfk-nl", load=true)
W, H, fitquality, robustness, aic = NMFk.load(nkrange, nruns; cutoff=0.3, resultdir=resultdir, casefilename="nmfk-nl");
Signals: 2 Fit: 186.979 Silhouette: 0.5600511 AIC: -193490.6 Signal order: [1, 2] Signals: 3 Fit: 116.1197 Silhouette: 0.3400365 AIC: -202059.3 Signal order: [1, 2, 3] Signals: 4 Fit: 81.67226 Silhouette: -0.004722083 AIC: -205653 Signal order: [1, 2, 3, 4] Signals: 5 Fit: 55.85581 Silhouette: 0.03561851 AIC: -210367.2 Signal order: [1, 2, 3, 4, 5] Signals: 6 Fit: 37.37445 Silhouette: 0.08498218 AIC: -215954.6 Signal order: [1, 2, 3, 4, 5, 6] Signals: 7 Fit: 22.77074 Silhouette: -0.06623857 AIC: -225288.2 Signal order: [1, 2, 3, 4, 5, 6, 7] Signals: 8 Fit: 14.94362 Silhouette: 0.0637157 AIC: -231651.2 Signal order: [1, 2, 3, 4, 5, 6, 7, 8] Signals: 9 Fit: 9.241436 Silhouette: -0.1125796 AIC: -240388.3 Signal order: [1, 2, 3, 4, 5, 6, 7, 8, 9] Signals: 10 Fit: 6.104004 Silhouette: -0.01305462 AIC: -246494 Signal order: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] ┌ Info: Optimal solution: 3 signals └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkIO.jl:30
NMFk.getks(nkrange, robustness[nkrange], 0.3)
2-element Vector{Int64}:
2
3
NMFk.getk(nkrange, robustness[nkrange], 0.3)
3
resultdirpost = "utah/results-postprocessing-nl-$(nruns)"
figuredirpost = "utah/figures-postprocessing-nl-$(nruns)"
NMFk.plot_feature_selecton(nkrange, fitquality, robustness; figuredir=figuredirpost)
Sorder, Wclusters, Hclusters = NMFk.clusterresults(NMFk.getk(nkrange, robustness[nkrange], 0.3), W, H, string.(collect(1:npoints)), attributes; lon=xcoord, lat=ycoord, resultdir=resultdirpost, figuredir=figuredirpost, ordersignal=:Wcount, Hcasefilename="attributes", Wcasefilename="locations", biplotcolor=:WH, sortmag=false, biplotlabel=:H, point_size_nolabel=2Gadfly.pt, point_size_label=4Gadfly.pt)
┌ Info: Number of signals: 3
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:205
┌ Info: Attributes (signals=3)
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:209
┌ Warning: type Clustering.KmeansResult{Core.Array{Core.Float32,2},Core.Float32,Core.Int64} not present in workspace; reconstructing
└ @ JLD /Users/vvv/.julia/packages/JLD/JHrZe/src/jld_types.jl:697
┌ Info: Robust k-means analysis results are loaded from file utah/results-postprocessing-nl-100/Hmatrix-3-3_18-1000.jld!
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:67
┌ Warning: Procedure to find unique signals could not identify a solution ...
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:158
┌ Warning: Procedure to find unique signals could not identify a solution ...
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:158
┌ Warning: Procedure to find unique signals could not identify a solution ...
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:158
┌ Warning: type Clustering.KmeansResult{Core.Array{Core.Float32,2},Core.Float32,Core.Int64} not present in workspace; reconstructing
└ @ JLD /Users/vvv/.julia/packages/JLD/JHrZe/src/jld_types.jl:697
┌ Info: Robust k-means analysis results are loaded from file utah/results-postprocessing-nl-100/Wmatrix-3-3_5218-1000.jld!
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:67
┌ Warning: Procedure to find unique signals could not identify a solution ...
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:158
┌ Warning: Procedure to find unique signals could not identify a solution ...
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:158
┌ Warning: Procedure to find unique signals could not identify a solution ...
└ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkCluster.jl:158
6×2 Matrix{Any}:
"pH" 1.0
"Temperature" 0.858848
"Ba" 0.84477
"Mg" 0.788068
"Ca" 0.76783
"HCO3" 0.549396
┌ Info: Signal B -> A Count: 6 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:316 ┌ Info: Signal A -> B Count: 9 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:316 ┌ Info: Signal C -> C Count: 3 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:316 ┌ Info: Signal A (S1) (k-means clustering) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:333
9×2 Matrix{Any}:
"δO18" 1.0
"Cl" 0.651079
"Na" 0.643991
"Br" 0.642053
"Quartz" 0.620207
"Chalcedony" 0.596872
"B" 0.586983
"K" 0.475275
"Li" 0.414782
3×2 Matrix{Any}:
"Be" 1.0
"Al" 0.672723
"TDS" 0.332944
┌ Info: Signal B (S2) (k-means clustering) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:333 ┌ Info: Signal C (S3) (k-means clustering) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:333
2131×2 Matrix{Any}:
"4816" 1.0
"4655" 0.970208
"4826" 0.958804
"2260" 0.955817
"4015" 0.938536
"3257" 0.93743
"4889" 0.929527
"1797" 0.924136
"3405" 0.924069
"2566" 0.905143
⋮
"350" 0.492666
"341" 0.491852
"2730" 0.487104
"3987" 0.47064
"483" 0.466211
"2909" 0.464978
"476" 0.46367
"2931" 0.435235
"452" 0.359449
1966×2 Matrix{Any}:
"4430" 1.0
"2992" 0.972819
"2309" 0.920633
"4913" 0.90427
"4056" 0.88218
"564" 0.868838
"4978" 0.866204
"4319" 0.857647
"4788" 0.855117
"2468" 0.831267
⋮
"375" 0.203084
"159" 0.202853
"3798" 0.198906
"5112" 0.1978
"738" 0.19415
"3317" 0.193811
"747" 0.189873
"10" 0.181605
"4954" 0.163099
1121×2 Matrix{Any}:
"5013" 1.0
"5118" 0.985958
"5064" 0.978501
"5068" 0.974911
"5126" 0.969774
"2554" 0.952582
"5049" 0.948387
"5048" 0.914722
"5006" 0.910949
"5095" 0.910366
⋮
"4154" 0.174466
"4710" 0.174457
"3376" 0.172963
"1412" 0.170218
"3662" 0.161509
"4300" 0.160252
"2742" 0.148677
"4289" 0.144853
"2028" 0.129906
┌ Info: Locations (signals=3) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:391 ┌ Info: Signal A (S1) Count: 2131 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:404 ┌ Info: Signal B (S2) Count: 1966 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:404 ┌ Info: Signal C (S3) Count: 1121 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:404 ┌ Info: Signal A -> A Count: 2131 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:414 ┌ Info: Signal B -> B Count: 1966 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:414 ┌ Info: Signal C -> C Count: 1121 └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:414 ┌ Info: Signal A (remapped k-means clustering) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:429 ┌ Info: Signal B (remapped k-means clustering) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:429 ┌ Info: Signal C (remapped k-means clustering) └ @ NMFk /Users/vvv/.julia/dev/NMFk/src/NMFkPostprocess.jl:429
([[1, 2, 3]], [['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B' … 'A', 'B', 'B', 'B', 'A', 'B', 'B', 'A', 'A', 'A']], [['A', 'B', 'B', 'A', 'C', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'B']])
Mads.display("utah/results-postprocessing-nl-100/attributes-3-groups.txt")
Signal A (S1) pH 1.0 Temperature 0.859 Ba 0.845 Mg 0.788 Ca 0.768 HCO3 0.549 Signal B (S2) δO18 1.0 Cl 0.651 Na 0.644 Br 0.642 Quartz 0.62 Chalcedony 0.597 B 0.587 K 0.475 Li 0.415 Signal C (S3) Be 1.0 Al 0.673 TDS 0.333
locations, lhs = DelimitedFiles.readdlm("utah/results-postprocessing-nl-100/locations-3.csv", ',', header=true)
clusters = sort(unique(locations[:,end]))
for i=1:size(clusters,1)
locations[locations .== clusters[i]] .= Integer(i)
end
locations = convert.(Float32, locations)
5218×7 Matrix{Float32}:
1.0 -111.06 39.4341 0.6627 0.0234368 0.124347 1.0
2.0 -111.066 39.4233 0.670377 0.0287214 0.14722 1.0
3.0 -111.053 39.4658 0.615474 8.88704f-7 0.203825 1.0
4.0 -111.07 39.4286 0.669782 0.0108548 0.0435491 1.0
5.0 -111.112 39.3211 0.640001 0.152409 0.00993296 1.0
6.0 -111.056 39.4947 0.763099 5.00038f-5 0.0934381 1.0
7.0 -111.056 39.4947 0.760624 0.0 0.0882335 1.0
8.0 -111.057 39.4947 0.69465 0.0 0.175217 1.0
9.0 -111.116 39.328 0.855406 0.0417626 0.00724127 1.0
10.0 -111.113 39.3455 0.528689 0.181605 0.00429201 2.0
⋮ ⋮
5210.0 -113.908 41.6355 0.575887 0.313012 0.12041 2.0
5211.0 -113.908 41.6369 0.497084 0.331514 0.00821709 2.0
5212.0 -113.909 41.6333 0.569316 0.312737 0.110429 2.0
5213.0 -113.935 41.5866 0.785556 0.214434 0.00475439 1.0
5214.0 -113.919 41.6333 0.607505 0.337506 0.103709 2.0
5215.0 -113.955 41.563 0.626563 0.314026 0.00605548 2.0
5216.0 -113.936 41.6496 0.723449 0.206588 0.00947504 1.0
5217.0 -113.926 41.7227 0.644214 0.224996 0.0861631 1.0
5218.0 -113.984 41.6888 0.70402 0.208866 0.0225921 1.0
GMT.grdimage("utah/maps/utah.nc", shade=(azimuth=100, norm="e0.8"), proj=:Mercator,
color=GMT.makecpt(color=:grayC, transparency=10, range=(0,5000,500), continuous=true),
figsize=8, conf=(MAP_FRAME_TYPE="plain", MAP_GRID_PEN_PRIMARY="thinnest,gray,.", MAP_GRID_CROSS_SIZE_SECONDARY=0.1, MAP_FRAME_PEN=0.5, MAP_TICK_PEN_PRIMARY=0.1, MAP_TICK_LENGTH_PRIMARY=0.01, FORMAT_GEO_MAP="ddd", FONT_ANNOT_PRIMARY=0.1, FONT_ANNOT_SECONDARY=0.1), frame=(axis="lrtb"))
GMT.legend!(box=(pen=false, fill=:white),
pos=(inside=true, anchor=:T, width=1.30, justify=:CM, offset=(-0.7, -0.6)),
GMT.text_record([
"S 0.10i c 0.10i red 0.25p 0.2i A"
"S 0.10i c 0.10i gold 0.25p 0.2i B"
"S 0.10i c 0.10i blue 0.25p 0.2i C"]),
par=(:FONT_ANNOT_PRIMARY, "8p,Arial"))
GMT.scatter!(locations[:,2], locations[:,3], marker=:c, markersize=:0.15,
color=(:red, :gold, :blue), zcolor=locations[:,end], alpha=10,
coast=(proj=:Mercator,
DCW=(country="US.UT", pen=(0.5,:black))),
fmt=:png, savefig="utah/maps/signatures-3")
Images.load("utah/maps/signatures-3.png")
**** WARNING: .setopacityalpha is deprecated (as of 9.53.0) and will be removed in a future release **** See .setfillconstantalpha/.setalphaisshape for the improved solution **** WARNING: .setopacityalpha is deprecated (as of 9.53.0) and will be removed in a future release **** See .setfillconstantalpha/.setalphaisshape for the improved solution pslegend [WARNING]: Representation of font type not recognized. Using default.
